Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations878
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory123.5 KiB
Average record size in memory144.0 B

Variable types

Numeric10
Categorical7

Alerts

APACHE II is highly overall correlated with Age and 2 other fieldsHigh correlation
Age is highly overall correlated with APACHE II and 1 other fieldsHigh correlation
Age_cat is highly overall correlated with AgeHigh correlation
Diagnosis is highly overall correlated with Diagnosis_catHigh correlation
Diagnosis_cat is highly overall correlated with DiagnosisHigh correlation
Gender is highly overall correlated with Gender_catHigh correlation
Gender_cat is highly overall correlated with GenderHigh correlation
LymC is highly overall correlated with NLCR and 1 other fieldsHigh correlation
Mortality is highly overall correlated with APACHE II and 1 other fieldsHigh correlation
NLCR is highly overall correlated with LymCHigh correlation
NeuC is highly overall correlated with WBCCHigh correlation
SOFA is highly overall correlated with APACHE II and 1 other fieldsHigh correlation
WBCC is highly overall correlated with LymC and 1 other fieldsHigh correlation
Mortality is highly imbalanced (75.8%) Imbalance
SOFA has 404 (46.0%) zeros Zeros

Reproduction

Analysis started2024-12-19 09:48:54.735920
Analysis finished2024-12-19 09:49:27.082168
Duration32.35 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.274487
Minimum18
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:27.410506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23
Q139
median57
Q370
95-th percentile85
Maximum99
Range81
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.216609
Coefficient of variation (CV)0.34765784
Kurtosis-0.9042444
Mean55.274487
Median Absolute Deviation (MAD)15
Skewness-0.10973703
Sum48531
Variance369.27805
MonotonicityNot monotonic
2024-12-19T09:49:28.176206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 28
 
3.2%
69 22
 
2.5%
58 22
 
2.5%
63 21
 
2.4%
52 21
 
2.4%
80 20
 
2.3%
64 20
 
2.3%
51 20
 
2.3%
39 19
 
2.2%
57 19
 
2.2%
Other values (68) 666
75.9%
ValueCountFrequency (%)
18 6
0.7%
19 7
0.8%
20 9
1.0%
21 5
 
0.6%
22 14
1.6%
23 10
1.1%
24 11
1.3%
25 7
0.8%
26 12
1.4%
27 10
1.1%
ValueCountFrequency (%)
99 1
 
0.1%
98 2
 
0.2%
97 1
 
0.1%
96 1
 
0.1%
94 3
 
0.3%
91 1
 
0.1%
89 4
 
0.5%
88 8
0.9%
87 10
1.1%
86 9
1.0%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
E
525 
K
353 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters878
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 525
59.8%
K 353
40.2%

Length

2024-12-19T09:49:28.812097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:49:29.246496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
e 525
59.8%
k 353
40.2%

Most occurring characters

ValueCountFrequency (%)
E 525
59.8%
K 353
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 525
59.8%
K 353
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 525
59.8%
K 353
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 525
59.8%
K 353
40.2%

Diagnosis
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
EC
551 
M
297 
AC
 
30

Length

Max length2
Median length2
Mean length1.6617312
Min length1

Characters and Unicode

Total characters1459
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowEC
3rd rowM
4th rowAC
5th rowAC

Common Values

ValueCountFrequency (%)
EC 551
62.8%
M 297
33.8%
AC 30
 
3.4%

Length

2024-12-19T09:49:29.498265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:49:29.737976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ec 551
62.8%
m 297
33.8%
ac 30
 
3.4%

Most occurring characters

ValueCountFrequency (%)
C 581
39.8%
E 551
37.8%
M 297
20.4%
A 30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 581
39.8%
E 551
37.8%
M 297
20.4%
A 30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 581
39.8%
E 551
37.8%
M 297
20.4%
A 30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 581
39.8%
E 551
37.8%
M 297
20.4%
A 30
 
2.1%

APACHE II
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.009112
Minimum0
Maximum34
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:30.029031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median10
Q314
95-th percentile25
Maximum34
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.4392545
Coefficient of variation (CV)0.58490228
Kurtosis0.98424134
Mean11.009112
Median Absolute Deviation (MAD)4
Skewness1.0783689
Sum9666
Variance41.463999
MonotonicityNot monotonic
2024-12-19T09:49:30.339011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
5 92
 
10.5%
10 75
 
8.5%
9 72
 
8.2%
7 65
 
7.4%
8 57
 
6.5%
11 49
 
5.6%
4 45
 
5.1%
12 43
 
4.9%
6 43
 
4.9%
14 42
 
4.8%
Other values (25) 295
33.6%
ValueCountFrequency (%)
0 4
 
0.5%
1 5
 
0.6%
2 7
 
0.8%
3 38
4.3%
4 45
5.1%
5 92
10.5%
6 43
4.9%
7 65
7.4%
8 57
6.5%
9 72
8.2%
ValueCountFrequency (%)
34 1
 
0.1%
33 4
0.5%
32 1
 
0.1%
31 3
 
0.3%
30 4
0.5%
29 7
0.8%
28 6
0.7%
27 4
0.5%
26 6
0.7%
25 9
1.0%

SOFA
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5284738
Minimum0
Maximum10
Zeros404
Zeros (%)46.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:30.607145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0603185
Coefficient of variation (CV)1.347958
Kurtosis2.9796836
Mean1.5284738
Median Absolute Deviation (MAD)1
Skewness1.7429532
Sum1342
Variance4.2449124
MonotonicityNot monotonic
2024-12-19T09:49:30.859474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 404
46.0%
2 164
18.7%
1 128
 
14.6%
3 60
 
6.8%
4 37
 
4.2%
5 26
 
3.0%
6 24
 
2.7%
7 13
 
1.5%
8 10
 
1.1%
9 7
 
0.8%
ValueCountFrequency (%)
0 404
46.0%
1 128
 
14.6%
2 164
18.7%
3 60
 
6.8%
4 37
 
4.2%
5 26
 
3.0%
6 24
 
2.7%
7 13
 
1.5%
8 10
 
1.1%
9 7
 
0.8%
ValueCountFrequency (%)
10 5
 
0.6%
9 7
 
0.8%
8 10
 
1.1%
7 13
 
1.5%
6 24
 
2.7%
5 26
 
3.0%
4 37
 
4.2%
3 60
 
6.8%
2 164
18.7%
1 128
14.6%

WBCC
Real number (ℝ)

High correlation 

Distinct698
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.010744
Minimum1.37
Maximum25.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:31.147483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.37
5-th percentile4.297
Q17.655
median10.525
Q314.12
95-th percentile19.633
Maximum25.6
Range24.23
Interquartile range (IQR)6.465

Descriptive statistics

Standard deviation4.6427384
Coefficient of variation (CV)0.4216553
Kurtosis-0.17212551
Mean11.010744
Median Absolute Deviation (MAD)3.19
Skewness0.48731764
Sum9667.433
Variance21.55502
MonotonicityNot monotonic
2024-12-19T09:49:31.456674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.58 4
 
0.5%
8.85 4
 
0.5%
8.03 3
 
0.3%
7.56 3
 
0.3%
10.64 3
 
0.3%
13.15 3
 
0.3%
5.09 3
 
0.3%
8.22 3
 
0.3%
12.41 3
 
0.3%
11.83 3
 
0.3%
Other values (688) 846
96.4%
ValueCountFrequency (%)
1.37 1
0.1%
1.88 1
0.1%
2.02 1
0.1%
2.19 1
0.1%
2.31 1
0.1%
2.41 1
0.1%
2.49 1
0.1%
2.52 1
0.1%
2.53 1
0.1%
2.69 1
0.1%
ValueCountFrequency (%)
25.6 1
0.1%
25.21 1
0.1%
24.82 1
0.1%
24.41 1
0.1%
23.7 1
0.1%
23.07 1
0.1%
23.05 2
0.2%
22.95 1
0.1%
22.92 1
0.1%
22.8 1
0.1%

NeuC
Real number (ℝ)

High correlation 

Distinct670
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2515945
Minimum0.92
Maximum21.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:31.767625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.92
5-th percentile3.367
Q16.0525
median8.69
Q312.1
95-th percentile16.8375
Maximum21.33
Range20.41
Interquartile range (IQR)6.0475

Descriptive statistics

Standard deviation4.1180599
Coefficient of variation (CV)0.44511894
Kurtosis-0.28058056
Mean9.2515945
Median Absolute Deviation (MAD)2.965
Skewness0.48659089
Sum8122.9
Variance16.958418
MonotonicityNot monotonic
2024-12-19T09:49:32.133366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.4 5
 
0.6%
5.73 5
 
0.6%
7.66 5
 
0.6%
9.07 5
 
0.6%
7.95 4
 
0.5%
8.35 4
 
0.5%
7.93 4
 
0.5%
5.21 4
 
0.5%
7.14 3
 
0.3%
8.79 3
 
0.3%
Other values (660) 836
95.2%
ValueCountFrequency (%)
0.92 1
0.1%
1.44 1
0.1%
1.54 1
0.1%
1.55 1
0.1%
1.64 1
0.1%
1.75 1
0.1%
1.8 1
0.1%
1.95 1
0.1%
1.99 1
0.1%
2.1 1
0.1%
ValueCountFrequency (%)
21.33 1
0.1%
21.05 1
0.1%
20.79 1
0.1%
20.62 1
0.1%
20.2 1
0.1%
20.1 1
0.1%
20 1
0.1%
19.69 1
0.1%
19.62 1
0.1%
19.59 1
0.1%

LymC
Real number (ℝ)

High correlation 

Distinct200
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94917995
Minimum0.11
Maximum2.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:32.475228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile0.3
Q10.58
median0.86
Q31.2375
95-th percentile1.9115
Maximum2.3
Range2.19
Interquartile range (IQR)0.6575

Descriptive statistics

Standard deviation0.48538434
Coefficient of variation (CV)0.5113723
Kurtosis-0.12184409
Mean0.94917995
Median Absolute Deviation (MAD)0.32
Skewness0.69553991
Sum833.38
Variance0.23559796
MonotonicityNot monotonic
2024-12-19T09:49:32.806493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.58 17
 
1.9%
0.46 15
 
1.7%
0.75 13
 
1.5%
0.61 12
 
1.4%
0.76 12
 
1.4%
0.86 11
 
1.3%
0.6 11
 
1.3%
0.53 11
 
1.3%
0.79 11
 
1.3%
0.52 10
 
1.1%
Other values (190) 755
86.0%
ValueCountFrequency (%)
0.11 2
 
0.2%
0.13 2
 
0.2%
0.14 1
 
0.1%
0.16 2
 
0.2%
0.17 1
 
0.1%
0.18 4
0.5%
0.2 1
 
0.1%
0.21 8
0.9%
0.22 2
 
0.2%
0.23 3
 
0.3%
ValueCountFrequency (%)
2.3 2
0.2%
2.29 2
0.2%
2.27 1
 
0.1%
2.26 1
 
0.1%
2.24 1
 
0.1%
2.23 4
0.5%
2.21 1
 
0.1%
2.2 1
 
0.1%
2.17 3
0.3%
2.14 1
 
0.1%

NLCR
Real number (ℝ)

High correlation 

Distinct853
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.421015
Minimum1.4099379
Maximum27.730159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:33.126166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.4099379
5-th percentile3.9055321
Q17.2431399
median10.423026
Q314.594867
95-th percentile23.082627
Maximum27.730159
Range26.320221
Interquartile range (IQR)7.3517268

Descriptive statistics

Standard deviation5.6053011
Coefficient of variation (CV)0.49078836
Kurtosis0.16219185
Mean11.421015
Median Absolute Deviation (MAD)3.5833333
Skewness0.7906158
Sum10027.651
Variance31.419401
MonotonicityNot monotonic
2024-12-19T09:49:33.446058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 6
 
0.7%
11 3
 
0.3%
4.235294118 2
 
0.2%
15 2
 
0.2%
15.63043478 2
 
0.2%
6 2
 
0.2%
14.5 2
 
0.2%
10.54545455 2
 
0.2%
12.375 2
 
0.2%
9.6 2
 
0.2%
Other values (843) 853
97.2%
ValueCountFrequency (%)
1.409937888 1
0.1%
1.833333333 1
0.1%
1.846153846 1
0.1%
2.040229885 1
0.1%
2.051546392 1
0.1%
2.100436681 1
0.1%
2.12 1
0.1%
2.12962963 1
0.1%
2.341013825 1
0.1%
2.448780488 1
0.1%
ValueCountFrequency (%)
27.73015873 1
0.1%
27.66666667 1
0.1%
27.58823529 2
0.2%
27.07142857 1
0.1%
26.98076923 1
0.1%
26.72972973 1
0.1%
26.25806452 2
0.2%
26.24242424 1
0.1%
26.23529412 1
0.1%
26.16666667 1
0.1%

PLTC
Real number (ℝ)

Distinct311
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.40433
Minimum13
Maximum414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:33.755088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile56
Q1123
median182
Q3234
95-th percentile339.3
Maximum414
Range401
Interquartile range (IQR)111

Descriptive statistics

Standard deviation83.124719
Coefficient of variation (CV)0.45323205
Kurtosis-0.31578696
Mean183.40433
Median Absolute Deviation (MAD)56
Skewness0.33312748
Sum161029
Variance6909.7189
MonotonicityNot monotonic
2024-12-19T09:49:34.118489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 11
 
1.3%
214 10
 
1.1%
189 9
 
1.0%
224 8
 
0.9%
158 7
 
0.8%
195 7
 
0.8%
201 7
 
0.8%
196 7
 
0.8%
105 7
 
0.8%
152 7
 
0.8%
Other values (301) 798
90.9%
ValueCountFrequency (%)
13 1
0.1%
14 1
0.1%
23 1
0.1%
24 1
0.1%
26 1
0.1%
30 1
0.1%
33 1
0.1%
35 1
0.1%
36 1
0.1%
38 1
0.1%
ValueCountFrequency (%)
414 1
 
0.1%
411 2
0.2%
404 1
 
0.1%
400 1
 
0.1%
394 1
 
0.1%
393 1
 
0.1%
392 3
0.3%
391 1
 
0.1%
389 2
0.2%
382 1
 
0.1%

MPV
Real number (ℝ)

Distinct52
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.15213
Minimum7.7
Maximum12.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:34.467865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7.7
5-th percentile8.7
Q19.5
median10.1
Q310.7
95-th percentile11.8
Maximum12.7
Range5
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.93069812
Coefficient of variation (CV)0.091675159
Kurtosis-0.13715545
Mean10.15213
Median Absolute Deviation (MAD)0.6
Skewness0.23663361
Sum8913.57
Variance0.86619899
MonotonicityNot monotonic
2024-12-19T09:49:34.792749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 56
 
6.4%
9.6 39
 
4.4%
10.1 39
 
4.4%
10.2 37
 
4.2%
10.4 37
 
4.2%
10.7 35
 
4.0%
10.6 35
 
4.0%
9.9 34
 
3.9%
11 32
 
3.6%
9.8 30
 
3.4%
Other values (42) 504
57.4%
ValueCountFrequency (%)
7.7 1
 
0.1%
7.9 2
 
0.2%
8 3
 
0.3%
8.1 4
 
0.5%
8.2 5
0.6%
8.3 3
 
0.3%
8.4 1
 
0.1%
8.5 8
0.9%
8.6 7
0.8%
8.7 12
1.4%
ValueCountFrequency (%)
12.7 4
0.5%
12.6 3
 
0.3%
12.5 3
 
0.3%
12.4 6
0.7%
12.3 2
 
0.2%
12.2 4
0.5%
12.1 9
1.0%
12 4
0.5%
11.9 5
0.6%
11.8 5
0.6%

Group
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
0
678 
1
200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters878
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 678
77.2%
1 200
 
22.8%

Length

2024-12-19T09:49:35.077954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:49:35.327587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 678
77.2%
1 200
 
22.8%

Most occurring characters

ValueCountFrequency (%)
0 678
77.2%
1 200
 
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 678
77.2%
1 200
 
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 678
77.2%
1 200
 
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 678
77.2%
1 200
 
22.8%

LOS-ICU
Real number (ℝ)

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7938497
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.7 KiB
2024-12-19T09:49:35.538566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2506267
Coefficient of variation (CV)0.69717473
Kurtosis1.7329815
Mean1.7938497
Median Absolute Deviation (MAD)0
Skewness1.6248408
Sum1575
Variance1.564067
MonotonicityNot monotonic
2024-12-19T09:49:35.780059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 538
61.3%
2 158
 
18.0%
3 77
 
8.8%
4 47
 
5.4%
5 46
 
5.2%
6 12
 
1.4%
ValueCountFrequency (%)
1 538
61.3%
2 158
 
18.0%
3 77
 
8.8%
4 47
 
5.4%
5 46
 
5.2%
6 12
 
1.4%
ValueCountFrequency (%)
6 12
 
1.4%
5 46
 
5.2%
4 47
 
5.4%
3 77
 
8.8%
2 158
 
18.0%
1 538
61.3%

Mortality
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
0
843 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters878
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 843
96.0%
1 35
 
4.0%

Length

2024-12-19T09:49:36.035118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:49:36.277876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 843
96.0%
1 35
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 843
96.0%
1 35
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 843
96.0%
1 35
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 843
96.0%
1 35
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 843
96.0%
1 35
 
4.0%

Gender_cat
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
1.0
525 
0.0
353 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2634
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 525
59.8%
0.0 353
40.2%

Length

2024-12-19T09:49:36.511026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:49:36.727698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 525
59.8%
0.0 353
40.2%

Most occurring characters

ValueCountFrequency (%)
0 1231
46.7%
. 878
33.3%
1 525
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1231
46.7%
. 878
33.3%
1 525
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1231
46.7%
. 878
33.3%
1 525
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1231
46.7%
. 878
33.3%
1 525
19.9%

Age_cat
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
0
538 
1
340 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters878
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 538
61.3%
1 340
38.7%

Length

2024-12-19T09:49:36.967727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:49:37.190388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 538
61.3%
1 340
38.7%

Most occurring characters

ValueCountFrequency (%)
0 538
61.3%
1 340
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 538
61.3%
1 340
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 538
61.3%
1 340
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 538
61.3%
1 340
38.7%

Diagnosis_cat
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.7 KiB
0.0
551 
1.0
297 
2.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2634
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 551
62.8%
1.0 297
33.8%
2.0 30
 
3.4%

Length

2024-12-19T09:49:37.446357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T09:49:37.671495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 551
62.8%
1.0 297
33.8%
2.0 30
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1429
54.3%
. 878
33.3%
1 297
 
11.3%
2 30
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1429
54.3%
. 878
33.3%
1 297
 
11.3%
2 30
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1429
54.3%
. 878
33.3%
1 297
 
11.3%
2 30
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1429
54.3%
. 878
33.3%
1 297
 
11.3%
2 30
 
1.1%

Interactions

2024-12-19T09:49:20.890024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:56.275689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:58.767792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:01.304981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:03.983599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:07.125869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:10.560149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:13.197078image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:15.539159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:18.066988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:21.312966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:56.557877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:59.024888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:01.556050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:04.230482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:07.571654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:10.827404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:13.437998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:15.820839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:18.314020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:21.724467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:56.822175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:59.284043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:01.802059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:04.502036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:07.971253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:11.057307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:13.688643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:16.101471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:18.574548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:22.057042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:57.082330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:59.515388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:02.034873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:04.780368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:08.329483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:11.288296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:13.929210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:16.344800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:18.824806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:22.437463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:57.310277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:59.772930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:02.281381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:05.104830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:08.736019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:11.504515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:14.143401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:16.592264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:19.037339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:22.877913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:57.561258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:00.035619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:02.547308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:05.472971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:09.089411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:11.757292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:14.390632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:16.848680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:19.322552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:23.257270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:57.787384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:00.279556image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:03.020547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:05.803590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:09.401014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:11.962351image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:14.605896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:17.094795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:19.550386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:23.698160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:58.019650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:00.509796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:03.246089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:06.163285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:09.735723image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:12.180860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:14.856813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:17.322968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:19.813142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:24.135687image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:58.286607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:00.808266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:03.520023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:06.533023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:10.093063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:12.422628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:15.112604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:17.589371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:20.159830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:24.521288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:48:58.511039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:01.042226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:03.759093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:06.856698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:10.323734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:12.976062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:15.322400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:17.818094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-19T09:49:20.532485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-19T09:49:37.873165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
APACHE IIAgeAge_catDiagnosisDiagnosis_catGenderGender_catGroupLOS-ICULymCMPVMortalityNLCRNeuCPLTCSOFAWBCC
APACHE II1.0000.5700.4570.3970.3970.0620.0620.4520.450-0.2230.0980.6660.054-0.215-0.1730.547-0.214
Age0.5701.0000.9060.2360.2360.1380.1380.2140.286-0.1340.0400.101-0.009-0.1640.0230.306-0.162
Age_cat0.4570.9061.0000.2740.2740.0450.0450.1360.2500.1110.0000.0890.0650.1050.1400.1970.127
Diagnosis0.3970.2360.2741.0001.0000.0000.0000.3800.3720.0390.1160.2440.1690.1070.1150.2940.093
Diagnosis_cat0.3970.2360.2741.0001.0000.0000.0000.3800.3720.0390.1160.2440.1690.1070.1150.2940.093
Gender0.0620.1380.0450.0000.0001.0000.9980.0000.0410.0380.0800.0000.0550.0640.0990.0280.044
Gender_cat0.0620.1380.0450.0000.0000.9981.0000.0000.0410.0380.0800.0000.0550.0640.0990.0280.044
Group0.4520.2140.1360.3800.3800.0000.0001.0000.3460.1640.0000.2410.0000.1170.1740.2440.112
LOS-ICU0.4500.2860.2500.3720.3720.0410.0410.3461.000-0.075-0.0120.198-0.008-0.113-0.0260.446-0.104
LymC-0.223-0.1340.1110.0390.0390.0380.0380.164-0.0751.000-0.0860.000-0.6180.4600.475-0.1130.555
MPV0.0980.0400.0000.1160.1160.0800.0800.000-0.012-0.0861.0000.1070.031-0.051-0.3730.122-0.057
Mortality0.6660.1010.0890.2440.2440.0000.0000.2410.1980.0000.1071.0000.0000.0860.0000.7090.000
NLCR0.054-0.0090.0650.1690.1690.0550.0550.000-0.008-0.6180.0310.0001.0000.355-0.109-0.0660.244
NeuC-0.215-0.1640.1050.1070.1070.0640.0640.117-0.1130.460-0.0510.0860.3551.0000.438-0.2030.982
PLTC-0.1730.0230.1400.1150.1150.0990.0990.174-0.0260.475-0.3730.000-0.1090.4381.000-0.2150.473
SOFA0.5470.3060.1970.2940.2940.0280.0280.2440.446-0.1130.1220.709-0.066-0.203-0.2151.000-0.191
WBCC-0.214-0.1620.1270.0930.0930.0440.0440.112-0.1040.555-0.0570.0000.2440.9820.473-0.1911.000

Missing values

2024-12-19T09:49:25.562358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-19T09:49:26.625511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderDiagnosisAPACHE IISOFAWBCCNeuCLymCNLCRPLTCMPVGroupLOS-ICUMortalityGender_catAge_catDiagnosis_cat
146EM7014.9211.912.125.6179252419.40101.001.0
465EEC1408.187.150.4117.43902415211.60201.010.0
580EM15115.4012.591.289.83593828011.01101.011.0
623EAC14010.488.661.227.09836117610.00101.002.0
753EAC10319.1616.280.6226.25806528010.80101.002.0
881EM11015.3113.581.2011.31666717710.90101.011.0
1147EEC505.164.490.2517.9600001169.00101.000.0
1253EEC765.034.520.3313.6969702412.01101.000.0
1320EM3247.515.320.757.0933332339.60301.001.0
1463EEC808.186.760.947.1914892089.60101.010.0
AgeGenderDiagnosisAPACHE IISOFAWBCCNeuCLymCNLCRPLTCMPVGroupLOS-ICUMortalityGender_catAge_catDiagnosis_cat
124360KEC1025.615.230.2322.739130859.90200.000.0
124540EEC3016.0813.641.1312.0707962039.30101.000.0
124631KEC728.076.640.5811.448276678.90300.000.0
124852EEC7016.7113.921.638.53987716010.60101.000.0
124959EEC1025.754.820.4610.4782611209.70101.000.0
125127EEC5019.6315.842.057.7268291948.80101.000.0
125241EEC723.352.880.309.6000004611.40101.000.0
125363EM1469.808.320.7610.94736813211.31301.011.0
125460EM2485.064.380.498.9387762568.00311.001.0
125631KM1658.406.690.768.8026321649.01400.001.0